Systems and methods for computer models of animal feed

ABSTRACT

The present application provides systems and methods for analyzing animal feeds and for adjusting animal feeds to improve the digestibility of animal feed components. Digestibility of animal feed can be determined by performing in vitro digestion of the feed and analyzing concentrations of residual components in the digested feed by NIR spectroscopy. Animal feed compositions can be adjusted to improve digestibility of components in the feed. The systems and methods of the present application can be used to determine the effect of an additive on the digestibility of feed.

FIELD

The present invention relates to systems and methods for adjustinganimal feeds. In particular, the present application relates to in vitrosystems and methods for analyzing animal feed for digestibility ofnutrients.

BACKGROUND

Nutrients in animal feed are made available to the animal by digestionof the feed in the animal's gastrointestinal tract. Nutrients that aredigested are absorbed and used by the animal for energy, growth, anddevelopment. Nutrients that are not digested, for the most part, passthrough the intestinal tract of the animal decreasing the nutritionalvalue of the feed. Digestibility of animal feed can be assessed by usingin-vitro or in vivo digestion models and analyzing remaining nutrientsin the digested feed by wet chemistry analytical methods. A drawback ofthe existing methods is that they are specific to a particular feed,expensive, and time consuming. It would, therefore, be beneficial toprovide for a broadly applicable, less expensive and less time-consumingway to analyze the digestibility of animal feed.

SUMMARY

The present application relates to systems and methods for analyzinganimal feeds and for adjusting animal feeds to improve the digestibilityof animal feed components. In particular, the present applicationrelates to in vitro digestion of a sample of animal feed and NIRanalysis, as defined herein, of the sample of digested feed to determinedigestibility of the animal feed components. In embodiments of thepresent application, a sample of an animal feed is digested using an invitro procedure that has been designed to be similar to in vivo animaldigestion. The sample of digested animal feed is scanned using NIRspectroscopy to generate spectral data that is compared to a computermodel to provide a predicted concentration of at least one residualcomponent in the sample of digested animal feed. The prediction of theconcentration of at least one residual component allows a determinationof the digestibility of that component in the animal feed composition.For example, protein is a component of animal feed compositions, andafter digestion, residual amounts of protein are determined and providea measure of the digestibility of the protein in the sample of animalfeed. Animal feed compositions can be selected and/or adjusted toimprove digestibility of components such as protein, phosphorus,carbohydrates, fats, gross energy, or fiber. Adjusting animal feedcompositions comprises adding one or more post additives. Variouspost-additives can be tested to determine whether such post-additivesimprove the digestibility of animal feed components.

The present application includes a method of analyzing feed comprisingdigesting a sample of animal feed in vitro using at least one enzyme togenerate digested animal feed comprising at least one residualcomponent; scanning the digested animal feed using NIR spectroscopy togenerate spectral data; and comparing the spectral data to a computermodel of the residual component to generate a predicted concentration ofthe at least one residual component of the digested animal feed. Methodsas described herein are methods of using NIR computer models to predictthe type and quantity of residual components of in vitro digested animalfeed sample. In embodiments, such methods are useful to select a feedcomposition and/or to adjust the feed composition to improvedigestibility of feed components.

In embodiments of the present application, digesting a sample of animalfeed comprises using pepsin or pancreatin or both. In other embodiments,digesting a sample of animal feed further comprises separating thedigested sample into a solid component and a liquid component, andscanning the digested animal feed comprises scanning the solidcomponent. In yet other embodiments, the at least one residual componentis selected from the group consisting of protein, phosphorous, fat,gross energy, carbohydrates, and fiber. In other embodiments, the sampleof animal feed comprises a pre additive. In embodiments, the preadditive comprises at least one enzyme.

In embodiments of the present application, the spectral data is comparedusing a computer implemented method comprising receiving spectral datafrom the digested sample and comparing the spectral data to a computermodel of the residual component to obtain a predicted concentration ofthe at least one residual component.

In embodiments of the present application, the method of claim 1,further comprises: adjusting the animal feed composition to obtain apredetermined nutritional profile for the animal based on the predictedconcentration of the at least one residual component of the sample ofdigested animal feed. In other embodiments, adjusting the animal feedcomposition to obtain a predetermined nutritional profile for the animalcomprises adding at least one post additive. In embodiments, the postadditive comprises an enzyme.

The present application further includes adjusting animal feedcomposition comprising steps of: identifying a predetermined nutritionalprofile of a feed component in the animal feed composition; predicting aconcentration of a residual component of the feed component in an animalfeed composition by a method comprising: digesting the sample of theanimal feed in vitro using at least one enzyme to generate digestedanimal feed comprising at least one residual component; scanning thedigested animal feed using NIR spectroscopy to generate spectral data;comparing the spectral data to a computer model of the at least oneresidual component to generate a predicted concentration of the residualcomponent; and adjusting the animal feed composition to obtain thepredetermined nutritional profile of the feed component based on thepredicted concentration of the at least one residual component. Inembodiments of the present application, the feed component is selectedfrom the group consisting of protein, phosphorous, fat, gross energy,carbohydrates, fiber and combinations thereof. In other embodiments, theat least one residual component is selected from the group consisting ofprotein, phosphorous, fat, gross energy, carbohydrates, and fiber.

In embodiments of the present application, digesting a sample of animalfeed comprises using pepsin or pancreatin or both. In other embodiments,digesting a sample of animal feed further comprises separating thedigested sample into a solid component and a liquid component, andscanning the digested animal feed comprises scanning the solidcomponent. In other embodiments, the sample of animal feed comprises apre additive. In embodiments, the pre additive comprises at least oneenzyme.

In embodiments of the present application, the spectral data is comparedusing a computer implemented method comprising receiving spectral datafrom the digested sample and comparing the spectral data to a computermodel of the residual component to obtain a predicted concentration ofthe at least one residual component.

In embodiments of the present application, adjusting the animal feedcomposition comprises adding a post additive to the feed. Inembodiments, the post additive comprises at least one enzyme. Inembodiments, the post additive adjusts the amount of the residualcomponent in the digested feed.

The present application also includes a method of developing a computermodel for analyzing feed comprising steps of: digesting a plurality ofsamples of animal feed in vitro using at least one enzyme to generate aplurality of digested animal feed samples, wherein each of the pluralityof digested animal feed samples comprises at least one residualcomponent; scanning each of the plurality of digested animal feedsamples using NIR spectroscopy to generate spectral data for each of theplurality of digested animal feed samples; determining the concentrationof the at least one residual component in each of the plurality ofdigested animal feed samples using a wet chemistry method; andgenerating a computer model by establishing a predictive relationshipbetween the concentration of the at least one residual component of eachof the plurality of digested animal feed samples to the spectral data ofa corresponding sample of the plurality of digested animal feed samples.

In some embodiments of the present application, the step of scanningeach of the plurality of digested feed samples further comprises thestep of mathematically manipulating the spectral data of each of theplurality of digested animal feed samples. In other embodiments,generating a computer model comprises a computer implemented methodcomprising steps of: receiving spectral data for each of the pluralityof digested animal feed samples; relating the spectral data for each ofthe plurality of digested animal feed samples to the concentration ofthe at least one residual component in a corresponding sample of theplurality of digested animal feed samples; and establishing a predictiverelationship based on the spectral data and the concentration of the atleast one residual component of the plurality of digested animal feedsamples to generate the computer model.

In embodiments of the present application, digesting the plurality ofsamples of animal feed comprises using pepsin or pancreatin or both. Inother embodiments, digesting a sample of animal feed further comprisesseparating the digested sample into a solid component and a liquidcomponent, and scanning the digested animal feed comprises scanning thesolid component. In yet other embodiments, the at least one residualcomponent is selected from the group consisting of protein, phosphorous,fat, gross energy, carbohydrates, and fiber.

In embodiments of the present application, the wet chemistry methodcomprises analyzing each sample of the plurality of digested feedsamples for the concentration of the at least one residual componentselected from the group consisting of protein, phosphorous, fat, grossenergy, carbohydrates, and fiber. In other embodiments, the wetchemistry method comprises mixing the solid component with a liquid toform a mixture; and analyzing the composition of the mixture for theconcentration of the at least one residual component selected from thegroup consisting of protein, phosphorous, fat, gross energy,carbohydrates, and fiber in each of the plurality of digested animalfeed samples.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a schematic of embodiments of methods of the presentapplication.

FIG. 2 shows an exemplary NIR spectral data of poultry feed samplesprocessed according to an embodiment of the present application.

FIG. 3 shows a NIR model for protein in poultry feed processed accordingto an embodiment of the present application.

FIG. 4 shows a NIR model for phosphorus in poultry feed processedaccording to an embodiment of the present application.

FIG. 5 shows a NIR model for gross energy in poultry feed processedaccording to an embodiment of the present application.

FIG. 6 shows exemplary NIR spectral data for swine feed samplesprocessed according to an embodiment of the present application.

FIG. 7 shows a NIR model for protein in swine feed processed accordingto an embodiment of the present application.

FIG. 8 shows a NIR model for gross energy in swine feed processedaccording to an embodiment of the present application.

DETAILED DESCRIPTION

Definitions

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e. to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

As used in this application, the term “additive(s)” refers to asubstance added to another substance. For example, an additive can beadded to animal feed to improve digestibility of one or more feedcomponents. An additive comprises an enzyme, a mixture of enzymes, aprotein, a vitamin, a mineral, grains, maltodextrin, a supplement, andcombinations thereof. A “pre-additive(s)” is a substance that is presentas a component of an animal feed sample and is not added to the feedsample at the time of or just prior to analysis. A pre-additive istypically already present in the animal feed used in the field andincludes, but is not limited to, an enzyme or mixture of enzymes. A“post-additive” is a substance that is added to the sample of animalfeed composition at the time of or just prior to analysis. Apost-additive is typically being added to the feed sample to determineif the post-additive alters the digestibility of a feed component andincludes, but is not limited to, an enzyme or mixture of enzymes.

As used in this application, the term “analyte(s)” refers to a chemicalconstituent, the properties of which (e.g., concentration) are to bedetermined using an analytical procedure.

As used in this application, the term “animal(s)” refers to non-humananimals raised or used as a source of food. For example, animalsinclude, but are not limited to, domesticated livestock such as cattle,goats, sheep, horses, poultry, buffalo, alpaca, llamas, donkeys, mules,rabbits, chickens, geese, turkeys, or pigs.

As used in this application, the term “computer model(s)” refers to amodel that is predictive of the concentration of a component of amixture (e.g., feed) based on spectral data obtained, for example, byNIR. In embodiments of the present application, the spectral data fromeach sample of a plurality of samples is related to a concentration of aresidual component as determined by analytical wet chemistry chemicalmethods for each sample. The model can be used to provide an estimationof the amount (e.g., concentration) of a constituent of an unknownsample by comparing the spectrum of the unknown sample to the model.

As used in this application, the terms “digesting”, “digested”, and“digestion” refer to changing a material by breaking down or decomposingits components. In some embodiments, digesting is an in vitro processusing, for example, heat, chemicals, and/or enzymes to break down thecomponents of the material.

As used in this application, the term “feed(s)” or “animal feed(s)”refers to material(s) that are consumed by animals and contribute energyand/or nutrients to an animal's diet. Animal feeds typically include anumber of different components that may be present in forms such asconcentrate(s), premix(es) co-product(s), or pellets. Examples of feedsand feed components include, but are not limited to, Total Mixed Ration(TMR), corn, soybean, forage(s), grain(s), distiller grain(s), sproutedgrains, legumes, vitamins, amino acids, minerals, molasses, fiber(s),fodder(s), grass(es), hay, straw, silage, kernel(s), leaves, meal,soluble(s), and supplement(s). Some components or constituents ofcomponents of the animal feed are detectable by Near InfraredSpectroscopy (NIR). Other components of animal feed are not detectableor may be poorly detectable by NIR because of low concentration,presence in a complex that masks the component, or because the physicalor chemical characteristics of the component do not lend themselves toNIR detection.

As used in this application, the term “in vivo” refers to processesoccurring within a living biological organism.

As used in this application, the term “in vitro” refers to processesoccurring in an artificial environment outside the living organism andto biological processes or reactions that would normally occur within anorganism but are made to occur in an artificial environment. In vitroenvironments can include, but are not limited to, test tubes and cellculture.

As used in this application, the term “nutrient(s)” refers to asubstance that is needed for an organism to live and/or grow. Nutrientsinclude, but are not limited to, compounds such as protein, fat,carbohydrates (e.g., sugars), fiber, vitamins, calcium, iron, niacin,nitrogen, oxygen, carbon, phosphorus, potassium, sodium chloride, andmixtures thereof.

As used in this application, the term “NIR spectroscopy” or “NIR” refersto the scanning and measuring the absorbance of samples using nearinfra-red radiation with wavelengths in the range of 800-2500 nm tocreate an absorbance spectrum. NIR is used to measure absorbance bychemical bonds caused by overtone and combination vibrations and is mostuseful as an indirect quantitative method of analysis. NIR spectroscopyis used to predict the amount or type of a chemical constituent of asubstance by comparing the spectrum obtained by scanning the sample to acalibration (e.g., a computer model). The spectra can be furthermanipulated mathematically, e.g., by Fourier transformation. NIRspectrometers can be configured to operate either in a reflectance ortransmittance mode. Specific examples of NIR equipment include BrukerMPA FT-NIR (available from Bruker Optics, Inc., Billerica, Mass.), andAntaris™ FT-NIR Analyzer (available from Thermo Scientific in Waltham,Mass.).

As used in this application, a “predetermined nutrition profile(s)”refers to a desired amount of a feed component or components in ananimal feed for which digestibility is a relevant characteristic. Anutritionist or a farmer can set a desired amount of a component in aparticular feed. For example, the amount of protein or additive in theanimal feed may need to be adjusted to take into account thedigestibility of the protein in the animal feed as determined using themethods described herein. An animal feed with protein that is in a formthat is less digestible may require an increase in protein in the animalfeed and/or the addition of a post additive that increases thedigestibility of protein in that animal feed to achieve the desiredamount.

As used in this application, a “predicted concentration(s)” refers to anamount of a nutrient or residual component detected in a digested feedsample using NIR spectroscopy and a computer model. In embodiments ofthe present application, the digested animal feed is scanned using NIRspectroscopy to generate spectral data; and the spectral data iscompared to a computer model of the at least one residual component togenerate a predicted concentration of the at least one residualcomponent of the digested animal feed. The predicted concentration is anestimate of the actual concentration of the at least one residualcomponent in the sample.

As used in this application, the term “residual component(s)” refers toa component that remains in a mixture after one or more components havebeen removed from and/or changed in the mixture. In embodiments of thepresent application, a residual component is an individual componentthat includes, but is not limited to, protein, phosphorous, fat,carbohydrates, and fiber. In other embodiments, a residual component isa characteristic of the digested sample including but not limited tomoisture content, gross energy, and ash content.

As used in this application, the term “sample(s) of animal feed” refersto a representative portion of an animal feed. In embodiments of thepresent application, a representative portion of an animal feed containsthe same components in similar proportions to that of the animal feed. Arepresentative sample is preferably homogenous or substantiallyhomogenous.

As used in this application, the term “spectral data” refers to the dataobtained when radiation interacts with a material. For example, spectraldata is obtained when radiation at near infrared wavelengths interactswith a material and is absorbed by vibrations of chemical bonds in thematerial. The intensity of the absorbance can be measured by measuringthe amount of radiation reflected back from or transmitted through thematerial at a given wavelength. The intensity of absorption at a givenwavelength responds to the amount and types of chemical bonds in thematerial.

Detailed Description

The present application relates to systems and methods for analyzinganimal feeds. In particular, the present application relates to systemsand methods for analyzing the digestibility of feed components in animalfeed. Additionally, the systems and methods of the present applicationare used to determine the effect of a pre-additive and/or apost-additive on the digestibility of a feed component.

Methods of analyzing animal feed for feed components involve procedures(e.g., digestion methods and wet chemistry methods) that are costly andtime consuming, often requiring multiple pieces of laboratory equipmentand multiple assays for different components. A single analysis usingwet chemical methods involves splitting a sample into different portionsfor analysis of residual components such as protein, sugars, phosphorus,gross energy, and fats. This analysis destroys the sample.

In contrast, the systems and methods provided in the present applicationprovide the predicted concentration of multiple components using asingle sample, are rapid, decrease the time and cost for analysis, andallow for adjusting feed composition to improve the digestibility offeed components in the animal feed composition.

Systems and Methods for Developing a Computer Model

The present application includes a method of developing a computer modelfor analyzing feed comprising steps of: digesting a plurality of samplesof animal feed in vitro using at least one enzyme to generate aplurality of digested animal feed samples, wherein each of the pluralityof digested animal feed samples comprises at least one residualcomponent; scanning each of the plurality of digested animal feedsamples using NIR spectroscopy to generate spectral data for each of theplurality of digested animal feed samples; determining the concentrationof the at least one residual component in each of the plurality ofdigested animal feed samples using a wet chemistry method; andgenerating a computer model by establishing a predictive relationshipbetween the concentration of the at least one residual component of eachof the plurality of digested animal feed samples to the spectral data ofa corresponding sample of the plurality of digested animal feed samples.

In Vitro Digestion Assay

To develop the computer model, a plurality of digested feed samples aregenerated and analyzed. Animal feed samples are digested in vitro usingat least one enzyme.

The components of animal feed samples vary depending on the source ofthe feed sample. For example, farms located in different geographicregions and/or having different breeds of an animal may have a feed withdifferent components. In addition, a nutritionist or a farmer can set adesired amount of a component in a particular feed or select aparticular feed based on the desired amount of the component. Examplesof feeds and feed components include, but are not limited to, TotalMixed Ration (TMR), corn, soybean, forage(s), grain(s), distillergrain(s), sprouted grains, legumes, vitamins, amino acids, minerals,molasses, fiber(s), fodder(s), grass(es), hay, straw, silage, kernel(s),leaves, meal, soluble(s), and supplement(s).

In embodiments of the present application, a plurality of differentanimal feed samples for a particular animal such as poultry or swine aredigested. The plurality of samples has a sufficient number of samples toprovide a computer model with a coefficient of determination (R²) valueof at least 50, 60, 70, 80, 90, or 100, or any number between 50 and100. In embodiments of the present application, a plurality of samplesincludes at least 25, 35, 50, or a 100 or more samples. In specificembodiments, a plurality of samples includes at least 50 unique samples.

In embodiments of the present application, each sample is digested invitro with at least one enzyme. In embodiments of the presentapplication, the enzyme and conditions of digestion are selected to besimilar to in vivo digestion of the type of animal. In embodiments, theanimal is a monogastric animal. In embodiments, the animal is a swine ora poultry. For example, one enzyme that is involved in digestion of thestomach is pepsin, and an enzyme involved in intestinal digestion ispancreatin. One or both of these enzymes are employed in the in vitrodigestion assay.

In embodiments of the present application, pepsin is used at an acidicpH of less than 7, 6, 5, 4, 3, 2 or any number in between. In someembodiments, the digestion with pepsin is conducted for a time thatcorresponds to in vivo digestion in the stomach of the animal, forexample, at least 1 to 6 hours for a swine. For example, about 30minutes to 2 hours for poultry. Conditions of pH, temperature, and timemay be adjusted depending on the type of animal.

In embodiments of the present application, a sample is digested withpancreatin. When a sample is digested with pancreatin, the digestion isconducted at a pH of at least 6.0. In some embodiments, the digestionwith pancreatin is conducted for a time that corresponds to in vivodigestion in the intestine of the animal, for example, at least 18 hoursto 48 hours for a swine. For example, about 30 minutes to 2 hours forpoultry. Conditions of pH, temperature, and time may be adjusteddepending on the type of animal.

In some embodiments, the sample of animal feed is digested with pepsinfollowed by digestion with pancreatin under conditions that are similarto in vivo digestion of the sample in the animal species. Animalsinclude animals raised or used a source of food including but notlimited to cattle, goats, sheep, horses, poultry, buffalo, alpaca,llamas, donkeys, mules, rabbits, chickens, geese, turkeys, or pigs.

In some embodiments of the present application, more or less steps maybe present in the in vitro digestion assay depending on the in vivodigestion process of the animal. For example, for swine, an in vitrodigestion assay includes, but is not limited to, a stomach phase, and anintestine phase. For example, for poultry an in vitro digestion assayincludes, but is not limited to, a crop phase, a gizzard phase, and asmall intestine phase. In stomach or gizzard phases, the digestion isconducted at an acidic pH and includes an enzyme like pepsin. In theintestine phase digestion is conducted at a neutral to slightly acidicpH and includes an enzyme like pancreatin. Other digestive enzymes maybe utilized. One or more digestive enzymes are employed in any one orany combination of phases.

Digested animal feed samples comprise at least one residual component. Aresidual component is a component may remain after digestion of a feedcomponent in the feed sample. For example, a feed component in a feedsample is protein but not all protein may be digested in vivo or in thein vitro assay so that a residual protein component remains afterdigestion. Other residual components include phosphorous, fat, grossenergy, carbohydrates, or fiber.

In embodiments of the present application, a method of digesting aplurality of samples comprises separating each sample into a liquidcomponent and a solid component. Typically such separation occurs usingcentrifugation, or filtration.

Each of the plurality of digested samples is analyzed by NIRspectroscopy and wet chemical methods.

NIR Spectroscopy

Each of the plurality of the digested samples of animal feed is scannedusing NIR spectroscopy to generate spectral data. Near InfraredSpectroscopy (NIR, also known as NIRS) is a spectroscopic technologythat is used for producing a predicted concentration of at least oneresidual component of the sample (e.g., the concentration of ananalyte). NIR relies on wavelengths in the range of 800-2500 nm, and ismost useful for measuring overtone and combination vibrations inmolecules. Because NIR measurements typically require little or nosample preparation, most samples can be measured without pretreatment,manipulation, or destruction.

A typical NIR instrument usually scans the sample multiple times acrossa selected wavelength range and averages the scans to produce aspectrum. NIR instruments can be configured to measure eithertransmittance of transparent samples or reflectance for opaque samples.Because of considerable overlap in overtone and combination bands ofmolecules, NIR technologies typically rely on multivariate calibrationtechniques. NIR computer models can comprise models (e.g., calibrations)for multiple analytes and can include tens, hundreds, or even thousandsof samples.

In embodiments of the present application, the spectral data isprocessed to place it in a form useful to generate a model. Inembodiments of the present application, the spectral data ismathematically manipulated to minimize noise, extract principalcomponents, and/or to subtract background.

In embodiments of the present application, the digested samples areseparated into a liquid and a solid component and the solid component isscanned. The solid component comprises at least one residual component.NIR scans can identify more than one component of a mixture as well asprovide a predicted concentration of each of the components in amixture. The NIR scans generate spectral data that is used to develop acomputer model for each of the residual components.

Chemical Analysis

The samples that are scanned on the NIR are also analyzed using primaryanalytical methods, i.e., wet chemistry methods. Wet chemistry methodsinclude primary reference methods used for the analysis of componentssuch as protein, phosphorous, fat, gross energy, carbohydrates, orfiber. Such assays are known to those to skill in the art. For protein,methods of analysis include determination of nitrogen and analysis byultraviolet visible spectroscopy. For phosphorus, methods of analysisinclude determination of phosphorus by inductively coupled plasmasystem. Gross energy can be determined in a bomb calorimeter.

Analytical chemical methods can be used on liquid or dry samples. Inembodiments of the present application, a digested sample is separatedinto a liquid component and a solid component. Both components can beanalyzed using wet chemistry methods. For example, released phosphoruscan be determined in the liquid component and residual phosphorus can bedetermined in the solid component. In some embodiments, the solidcomponent is mixed with a liquid in order to facilitate wet chemicalanalysis of at least one residual component in the solid component.

The wet chemical analysis provides a concentration of at least oneresidual component in each of the digested samples. In embodiments ofthe present application, multiple residual components are measured.

Generating a Computer Model

In embodiments of the present application, a method comprises generatinga computer model by establishing a predictive relationship between theconcentration of the at least one residual component of each of theplurality of digested animal feed samples to the spectral data of acorresponding sample of each of the plurality of digested animal feedsamples.

The spectra of various samples are related to wet chemistry resultsusing mathematical manipulation of data via a computer implementedmethod to produce the calibration. In embodiments of the presentapplication, a computer implemented method comprises steps of: receivingspectral data for each of the plurality of digested animal feed samples;relating the spectral data for each of the plurality of digested animalfeed samples to the concentration of the at least one residual componentin a corresponding sample of each of the plurality of digested animalfeed samples; and establishing a predictive relationship based on thespectral data and the concentration of the at least one residualcomponent in each of the plurality of digested animal feed samples togenerate the computer model.

Spectral data is generated upon scanning the digested samples. Spectraldata may be mathematically processed to place it in a form useful togenerate a model. In embodiments of the present application, thespectral data is mathematically manipulated to minimize noise, extractprincipal components, and/or to subtract background.

Spectral data from each sample is then related to the concentration ofat least one residual component of the corresponding sample asdetermined by the wet chemical methods. Relating of the spectral data tothe concentration of at least one residual component occurs when theconcentration of the at least one residual component for the particularsample are input into the NIR spectrometer.

The relationship between the concentration of at least one residualcomponent in each of the plurality of samples is used to generate amodel using one or more statistical methods to establish a predictedrelationship between the concentration of the at least one residualcomponent in a sample and the spectral data for that data. Statisticalmethods include principal component analysis, linear regressionanalysis, or partial least squares analysis. Any number of statisticalmethods can be used to build a computer model for that residualcomponent.

In embodiments of the present application, NIR models are characterizedby coefficient of determination, R² value, that reflects the predictivepower of the computer model. In embodiments of the present application,the R² values of the computer model are at least 50, 60, 70, 80, 90, or100, or any number between 50 and 100.

A computer model is typically validated using a validation method. Inembodiments of the present application, a validation method is acomputer implemented method where the plurality of samples is dividedinto a model building set and a validation set. Assignment of thesamples to a set is typically done randomly. The data from the modelbuilding set are utilized to build a model as described herein. The datafrom the validation set are used to test the predictive power of themodel. The samples of the validation set are tested against the model togenerate a predicted concentration of the at least one residualcomponent. This predicted concentration is then compared to the actualconcentration of the sample as determined by wet chemistry. Thiscomparison allows a determination of R² value and/or standard error.Other types of validation such as a leave one out validation method canalso be employed.

Once the computer model is generated it is stored within the NIRspectrometer. In embodiments of the present application, NIRspectrometer includes a microprocessor and memory having instructions toimplement the computer implemented of generating a computer model orvalidating a computer model as described herein. In embodiments of thepresent application, the memory serves to store computer models for eachresidual component, and/or a database of spectral data for each sample.The computer model is useful to provide for a predicted concentrationthe at least one residual component of a sample with an unknownconcentration of the at least one residual component.

According to embodiments of the present application, the systems of thepresent application include systems and equipment suitable forperforming in vitro digestion of feed samples and NIR analysis ofdigested samples. According to other embodiments, the systems of thepresent application also include systems and equipment suitable forperforming wet chemistry analysis of digested samples. According toexemplary embodiments, the system may comprise typical laboratoryequipment and glassware, such as flasks, beakers, test tubes, scales,pipettes, incubators, shakers, stirrers, water baths, etc. The systemmay also comprise analyzers, such as an ICP, a nitrogen analyzer, and abomb calorimeter.

According to embodiments, the system further comprises a NIR analyzerequipped with a computer and software suitable for operating the NIR andfor developing and using a computer model (e.g., calibration). Accordingto an alternative embodiment, the computer model may be stored on aremote computer, accessed by the computer via communicationsinfrastructure, such as the internet. In an exemplary embodiment, theNIR is configured with a rotating sample cup assembly for scanning feedsamples.

The methods and systems of the present application can be implemented asa combination of hardware and software. The software can be implementedas an application program tangibly embodied on a program storage device,or different portions of the software implemented in the user'scomputing environment (e.g., an applet) and on a reviewer's computingenvironment, where the reviewer may be located at a remote site (e.g.,at a service provider's facility).

In the embodiments, the computer includes a processor unit. Theprocessor unit operates to receive information, which generally includesspectral data (e.g., NIR spectra), and a database of known data (e.g.,experimentally determined information (e.g., wet chemistry results) froma plurality of samples). This information received can be stored atleast temporarily in a database, and data analyzed.

For example, during or after data input by the user, portions of thedata processing can be performed in the user-side computing environment.For example, the user-side computing environment can be programmed toprovide for defined test codes to denote platform, carrier/diagnostictest, or both; processing of data using defined flags, and/or generationof flag configurations, where the responses are transmitted as processedor partially processed responses to the reviewer's computing environmentin the form of test code and flag configurations for subsequentexecution of one or more algorithms to provide a results and/or generatea report in the reviewer's computing environment.

Systems and Methods for Analyzing Feed Samples

The present application includes methods of using a computer model of atleast one residual component of an animal feed. Such methods are usefulto compare different feed compositions and to adjust feed compositionsto improve digestibility of feed components. In embodiments of thepresent application, a method of analyzing feed comprises steps of:digesting a sample of animal feed in vitro using at least one enzyme togenerate digested animal feed comprising at least one residualcomponent; scanning the digested animal feed using NIR spectroscopy togenerate spectral data; and comparing the spectral data to a computermodel of the at least one residual component to generate an predictedconcentration of the at least one residual component of the digestedanimal feed.

As described previously, the samples of animal feeds can differ incomponents and can be obtained from different sources. In embodiments ofthe present application, a sample of animal feed comprises apre-additive. A pre-additive includes an enzyme. Feed samples aredigested using in vitro digestion with at least one enzyme as describedherein. Samples used in this method have an unknown amount of at leastone residual component after digestion.

The samples are being analyzed, for example, to identify theconcentration of the at least one residual component in the digestedsample. Residual components include, but are not limited to, protein,phosphorous, fat, gross energy, carbohydrates, and fiber. Analysis ofresidual components is useful to determine the digestibility of the feedsample. Digested samples are scanned using NIR spectroscopy and thespectra are stored in the NIR spectrometer. Feed samples of differenttypes can be compared to one another in order to identify which feedcomposition provides for greater digestibility of the feed component.For example, if a first feed composition has a lower residual protein,gross energy or phosphorus component after digestion than another feedcomposition, then the first feed composition is selected.

When the computer model is used to analyze samples, the spectrumproduced by scanning the sample is compared to the model that thenreturns a predicted concentration of the composition of the sample. ANIR measurement typically lasts only a few minutes and returns resultsimmediately, making NIR measurements fast and convenient.

According to embodiments of the present application, NIR measurementsare combined with an in vitro digestion assay to determine thedigestibility of a sample. Existing NIR methods include scanning feedsamples as-is (i.e., without sample pre-treatment) and estimating theamount of a component of a feed sample. However, such methods only workto predict the initial composition of the feed and are not able todifferentiate which feed samples will have improved digestibility.Therefore, a method has been developed where feed samples are processedusing a digestion assay and the digested sample is scanned using NIR,allowing for a more accurate prediction of the digestibility of thecomponents of the feed sample, while saving time and resources becausethe digested sample does not need to be analyzed using wet chemistrymethods. The method combines the information conveyed by the digestionassay and the speed and convenience of NIR measurement.

In embodiments of the present application, the present applicationincludes digesting a first sample of an first animal feed compositionand digesting a second sample of a second animal feed composition withat least one enzyme, wherein the first and second feed compositiondiffer from one another in at least one feed component; scanning thefirst and second sample of the digested animal feed using NIRspectroscopy to generate spectral data for at least one residualcomponent of each sample; comparing the spectral data from each sampleto a computer model of the at least one residual component to generate apredicted concentration of the at least one residual component of thefirst and second sample of digested animal feed; and selecting theanimal feed composition that has the desired or predeterminedconcentration of the at least one residual component by comparing thepredicted concentration of the at least one residual component of thefirst and second sample of digested animal feed. In some embodiments, ananimal feed composition is selected that provides for a decrease in aresidual component such as protein, phosphorus or gross energy.

In embodiments of the present application, a method provides fordetermining the effect of a post-additive on digestibility of the feedsample. Such methods are useful to determine whether adding a postadditive to a feed composition improves the digestibility of the feed orprovides a feed composition that has a predetermined nutritional profileof a feed component. In embodiments of the present application, one ormore post-additives are added to the feed sample prior to or at the timeof digestion. Different additives can be compared for the ability toimprove digestibility of a feed component. In embodiments of the presentapplication a post-additive comprises at least one enzyme, a mixture ofenzymes, or a substrate with a microbial source of enzymes.

In the present application, methods are also useful to compare theefficiency of feed compositions with different components. In that case,a first feed sample has a first composition, a second feed compositionhas a second composition, wherein the first and second feed compositionsdiffer from one another in at least one feed component. In embodiments,the first and second feed compositions differ from one another by havinga different component or the same component but in different amounts. Inembodiments, the components that differ in presence or amount areselected from the group consisting of phosphorous, fat, protein, grossenergy, carbohydrates, fiber, a pre-additive, and combinations thereof.

In embodiments of the present application, the present applicationincludes digesting a first sample of an first animal feed compositionand digesting a second sample of a second animal feed composition withat least one enzyme, wherein the second feed composition differs fromthe first composition by the presence of at least one post-additive orby having a different post-additive; scanning the first and secondsample of the digested animal feed using NIR spectroscopy to generatespectral data for at least one residual component of each digestedsample; comparing the spectral data from each sample to a computer modelof the at least one residual component to generate a predictedconcentration of the at least one residual component of the first andsecond sample of digested animal feed; and selecting the animal feedcomposition that has the desired or predetermined concentration of theat least one residual component by comparing the predicted concentrationof the at least one residual component of the first and second sample ofdigested animal feed. In some embodiments, a post-additive is selectedthat provides for a decrease in a residual component such as protein,phosphorus or gross energy.

Systems and Methods for Adjusting Animal Feed

The present application includes methods of using a computer model of atleast one residual component of an animal feed. Such methods are usefulto compare different feed compositions and to adjust feed compositionsto improve digestibility of feed components. In embodiments of thepresent application, adjusting animal feed composition comprises stepsof: identifying a predetermined nutritional profile of a feed componentof the animal feed composition; predicting a concentration of a residualcomponent of the feed component in an animal feed by a methodcomprising: digesting a sample of the animal feed in vitro using atleast one enzyme to generate digested animal feed comprising at leastone residual component; scanning the digested animal feed using NIRspectroscopy to generate spectral data; comparing the spectral data to acomputer model of the at least one residual component to generate apredicted concentration of the residual component; and adjusting theanimal feed composition to obtain the predetermined nutritional profileof the feed component based on the predicted concentration of the atleast one residual component.

Accordingly, in some embodiments, the present invention provides anefficient way to analyze feed (e.g., animal feed) for enzymatic effectson protein, fat, gross energy, digestible energy, phosphorous release,sugar release, fiber, carbohydrates, etc., and to determine the effect apost-additive has on the digestibility of the feed. The systems andmethods described in the present application can be used to analyze amultitude of feeds for multiple components and can be updated rapidlywithout undergoing in vivo trials.

The present application finds use in the analysis of any number ofanimal feeds and is not limited to analysis of a particular feed. Animalfeed is any foodstuff that is used specifically to feed domesticatedlivestock (e.g., cattle, goats, sheep, horses, poultry, buffalo, alpaca,llamas, donkeys, mules, rabbits, and pigs). Animal feeds often includeTotal Mixed Ration (TMR), corn, soybean, forage(s), grain(s), distillergrain(s), sprouted grains, legumes, vitamins, amino acids, minerals,molasses, fiber(s), fodder(s), grass(es), hay, straw, silage, kernel(s),leaves, meal, soluble(s), and supplement(s).

The digestibility of a feed component can be improved by adding one ormore post-additives such as enzymes (e.g., digestive enzymes) to thefeed. For example, enzymes such as phytase, protease, fungal protease,cellulase, xylanase, acid phosphatase, beta-glucanase, pectinase, andalpha amylase, can be added to the feed (i.e., used as additives) toimprove digestibility. The enzymes may be provided in purified form,partially purified form, or crude form. The enzymes may be of natural(e.g., fungal) or synthetic origin, or may be produced in vitro (e.g.,recombinant). In some embodiments, a protease (e.g., pepsin) is added.In some embodiments, commercially available enzyme or enzyme mixturesare added (e.g., Allzyme SSF, available from Alltech, Nicholasville,Ky.).

In order to determine whether adding a post-additive to a particularfeed composition would be desirable, it is beneficial to know thedigestibility components of the feed composition. According to anembodiment, the feed composition can be analyzed by digesting a sampleof the animal feed in vitro to generate digested feed comprising atleast one residual component, scanning the digested feed using NIR togenerate spectral data, comparing the spectral data to a computer modelof at least one residual component, and generating a predicted of theconcentration of the residual component. The predicted of theconcentration of the one or more residual components can be compared toa predeteimined or desired nutritional profile of the feed component. Insome embodiments of the present application, if the amount of theresidual component is higher than in the predetermined or desirednutritional profile of the feed component, the nutritional profile canbe enhanced by adding one or more post-additives, such as enzymes (e.g.,digestive enzymes).

A “predetermined nutrition profile(s)” refers to a desired amount of afeed component or components in an animal feed for which digestibilityis a relevant characteristic. A nutritionist or a farmer can set adesired amount of a component in a particular feed. For example, theamount of protein or additive in the animal feed may need to be adjustedto take into account the digestibility of the protein in the animal feedas determined using the methods described herein. An animal feed withprotein that is in a form that is less digestible may require anincrease in protein in the animal feed and/or the addition of a postadditive that increases the digestibility of protein in that animal feedto achieve the desired amount.

According to another embodiment, the system and method can be used tocompare the digestibility or two or more feeds, one or more of which maycomprise a post-additive, such as an enzyme (e.g., digestive enzyme).For example, the system and method can be used to show that one feedsample has a superior nutritional profile as compared to another feedsample because the one feed sample has a component that is moredigestible.

The following examples are provided in order to demonstrate and furtherillustrate certain preferred embodiments and aspects of the presentinvention and are not to be construed as limiting the scope thereof.

EXAMPLES Example 1 NIR Models

NIR models were created for digested samples of swine and poultry feed.The models can be used in conjunction with a digestion assay to evaluatethe digestibility of feed samples by estimating the content of residualcomponents in the digested samples.

FIG. 1 shows an exemplary scheme for methods of digesting and analyzinga feed sample as described herein. The figure shows that an initial feedsample was subjected to in vitro digestion and the digest was analyzedfor residual amounts of gross energy, protein, phosphorus, and sugarcontent using NIR and wet chemistry.

Samples of poultry and swine feed were digested using a digestion assaythat is similar to digestion of nutrients in vitro. The assay wasmodified from Boisen S., A multienzyme assay for pigs, Chapter 10, AModel for Feed Evaluation Based on Invitro Digestible Dry Matter andProtein, Invitro Digestion for Pig and Poultry, 1990, M. F. Fuller.

The digested samples were analyzed using wet chemistry methods forprotein, gross energy and phosphorus. A final mass consisting ofdigested feed and liquid was separated into a solid component and aliquid component. The solid component was dried and the dried solidsscanned using a NIR spectrometer. NIR models were created for protein,phosphorus, and gross energy in digested poultry feed, and for proteinand gross energy in digested swine feed.

Digestion Assay

Samples of swine and poultry feed were digested using the followingdigestion assay. Some samples were altered by adding a post-additive(Allzyme SSF) during the assay.

Starting Materials:

Poultry and swine feed samples of various origins were used. Bothpoultry and swine feed samples were mainly composed of corn and soybeanmeal.

Allzyme SSF feed additive, available from Alltech, Nicholasville, Ky.was used as a source of added enzymes (e.g post additive) to the feed.Allzyme SSF contains an enzyme complex including at least 300 U phytase.

Reagents:

-   A. HCl: 0.2 M, 1M, 2 M, and 4 M-   B. 0.6 M NaOH-   C. 1M NaHCO₃-   D. Pepsin from Sigma (P7012); 10 mg pepsin/mL de-ionized water or    2.25 mg pepsin/mL de-ionized water-   E. Pancreatin from Sigma (P3292); 50 mg pancreatin/mL de-ionized    water or 2.315 mg pancreatin/mL de-ionized water-   F. 15% trichloroacetic acid (TCA)-   G. Acetate buffer: 0.1 M (pH 6.0) and 0.2 M (pH 6.8)-   H. Chloramphenicol solution; 5 mg chloramphenicol/1 mL alcohol-   I. TCA stop solution-   J. Color Reagent prepared from 3 volumes of 1 M Sulfuric Acid, 1    volume of 2.5% (w/v) Ammonium Molybdate, and 1 volume of 10% (w/v)    Ascorbic Acid-   K. DNS solution (stored in a dark bottle), prepared from    dinitrosalisylic acid, NaOH, potassium sodium tartrate tetrahydrate    and de-ionized water-   L. Dextrose standards: 0 mg/mL; 0.2 mg/mL; 0.4 mg/mL; 0.6 mg/mL; 0.8    mg/mL; and 1.0 mg/mL    -   Phosphate standards: 0 μM; 5.625 μM; 11.25 μM; 22.5 μM; 45 μM;        and 90 μM potassium phosphate in water.        Procedure        Enzyme Additive

To produce a liquid enzyme product to be used in the experiment as apost-additive, enzymes were extracted from Allzyme SSF with de-ionizedwater and diluted 1:2,500,000 with 0.1 M sodium acetate buffer.

Swine Digestion Assay

Primary Digestion—Stomach

Two grams of a ground swine feed sample were mixed with 49 mL of 0.1 Msodium acetate buffer. Some samples were altered by adding apost-additive by mixing with 1 mL of the liquid post additive preparedfrom the Allzyme SFF product described above. The pH of the solution wasadjusted to 2 with HCl. 2.0 mL of pepsin solution (10 mg pepsin/mLde-ionized water) and 1.0 mL chloramphenicol solutions were added. Thesolution was stirred and placed in a 39° C. agitating water bath at 55RPM for 6 hrs. The solution was stirred hourly.

Secondary Digestion—Small Intestine

After primary digestion, the samples were mixed with 20 mL of 0.2 Msodium acetate buffer and 10 mL of 0.6 M NaOH. The pH of the solutionwas adjusted to 6.8 with 0.6 M NaOH. 2.0 mL of pancreatin solution (50mg pancreatin/mL de-ionized water) was added. The solution was stirredand placed in a 39° C. agitating (55 RPM) water bath for 18 hours. Thesolution was stirred and centrifuged at 14000 g for 20 min.

Poultry Digestion Assay

Crop Phase

Two and a half grams of a ground poultry feed sample were mixed with 6ml of distilled water. Some samples were altered by adding apost-additive by mixing with 1 mL of the liquid post additive preparedfrom the Allzyme SFF product described above with 7 ml. of distilledwater. The samples were incubated at 40° C. for 30 minutes.

Gizzard Phase

After incubation in the crop phase samples were adjusted to pH 3.0 with1M HCl. 2.0 mL of pepsin solution (2.25 mg pepsin/mL de-ionized water)and 1.0 mL chloramphenicol solutions were added to each sample. Thesolution was stirred and placed in a 40° C. water bath for 45 minutes.

Small Intestine

After the gizzard phase, the samples were mixed with 1 mL of NaHCO₃ toobtain a pH of 6.5. 2.0 mL of pancreatin solution (2.315 mgpancreatin/mL de-ionized water) was added. The solution was stirred andplaced in a 40° C. water bath for 60 minutes. The solution was stirredand centrifuged at 14000 g for 20 min.

Wet Chemistry Analysis

The in vitro digestion procedure left a final mass consisting of a solidportion of digested feed and a liquid portion. The sample was separatedfor further analysis into a solid component and a liquid component(i.e., supernatant). The solid component was freeze dried to give afinal dry matter portion.

-   Equipment:-   Varian 720-ES ICP for phosphorus analysis-   Leco TruSpec CHN for nitrogen (protein) analysis-   Parr 6100 bomb calorimeter for gross energy analysis    Protein, Phosphorus and Gross Energy

The final dry matter portion of the digested sample was analyzed forprotein, phosphorus and gross energy. Protein content was determinedusing a nitrogen combustion analyzer and by converting the nitrogencontent to protein. Phosphorus content was determined using an ICP(inductively coupled plasma) system. Gross energy content was determinedusing a bomb calorimeter.

NIR Model

The final dry matter portion of the digested sample was scanned using aNIR rotating cup assembly to collect NIR reflectance data. The NIR scansof various samples were recorded and were correlated with the wetchemistry results for each sample to create a computer model for eachcomponent (protein, phosphorus and gross energy) of the digestedsamples.

Equipment:

-   Bruker MPA FT-NIR, model number 122000 with rotating cup assembly,    available from Bruker Optics, Inc., Billerica, Mass.    Settings:-   Resolution 16 cm⁻¹-   Scans 32: the instrument was set to scan each sample 32 to times and    to average the scans into a single scan file for each sample-   Wavenumber range 10000 cm⁻¹−4000 cm⁻¹-   Absorbance was measured using the “sphere macrosample” compartment    Results—Poultry Feed Digestion Assay

Poultry feed samples were digested according to the digestion assay.Final dry matter portions of the digested samples were scanned on theNIR. An exemplary NIR scan of the digested poultry feed samples areshown in FIG. 2.

The digested samples were analyzed by wet chemistry methods for protein,phosphorus and gross energy. The protein content of the digested sampleswas in the range of 13-34%; the phosphorus content was in the range of750-5900 ppm; and gross energy in the range of 3300-5200 cal/g. Theprotein, phosphorus and gross energy content of each sample were thencorrelated with the NIR scans to develop NIR models for each component.

A computer-generated cross validation of the NIR protein model forpoultry feed is shown in FIG. 3. The model included 29 samples. The R²for the model was 86.66 and the RMSECV (root mean square error ofcross-validation) was 2.68. R² value indicates how well the data fitsthe model and how well the observed outcomes are predicted by the model.The RMSECV is a measure of the variation in the data.

A computer-generated cross validation of the NIR phosphorus model forpoultry feed is shown in FIG. 4. The model included 24 samples. The R²for the model was 75.94 and the RMSECV was 554.

A computer-generated cross validation of the NIR gross energy model forpoultry feed is shown in FIG. 5. The model included 18 samples. The R²for the model was 87.72 and the RMSECV was 148. It is expected that R²and RMSEV of the models can be improved by increasing the number ofsamples in the model.

Results—Swine Feed Digestion Assay

Swine feed samples were digested according to the digestion assay. Thefinal dry matter portions of digested samples were scanned using the NIRrotating cup assembly to collect NIR reflectance data. An exemplary scanof the digested swine feed samples is shown in FIG. 6.

The digested samples were analyzed by wet chemistry methods for proteinand gross energy. The protein content of the digested samples was in therange of 6-47% and gross energy in the range of 3900-5300 cal/g. Theprotein and gross energy content of each sample was then correlated withthe NIR scans to develop NIR models for each component.

A computer-generated cross validation of the NIR protein model for swinefeed is shown in FIG. 7. The R² for the model was 98.86 and the RMSECVwas 0.708.

A computer-generated cross validation of the NIR gross energy model forswine feed is shown in FIG. 8. The R² for the model was 80.5 and theRMSECV was 141.

DISCUSSION

The results show various NIR models that can be developed for use inconjunction with a digestion assay to predicted the content ofun-digested (i.e., residual) components in digested feed samples. Theprotein model for swine feed was particularly successful (R² of 98.86),even with a relatively low number of samples used in the exemplarymodel. The model was shown to be highly predictable of actual proteincontent in digested feed samples and can be used in lieu of performingexpensive and time-consuming wet chemistry methods. The accuracy of themodels can be increased with an increased number of samples. Inparticular, it is expected that the predictability of the phosphorus andgross energy models can be improved by including more samples in themodels.

Example 2 Validation of the Residual Protein Model for Swine

The predictability of the residual protein model for digest of swinefeed was validated. The method of validating is reflective of how themethods described herein are used to analyze a feed sample with unknowncharacteristics.

A set of samples of swine feed was digested as described in Example 1and scanned using NIR. Each sample was also characterized for proteincontent using wet chemistry. The samples were divided into a validationset and model building set. The samples in the model building set wereused to create a model as described herein. The validation set wastested in the model to generate a predicted concentration of residualprotein for each sample. The predicted concentration for each sample wascompared to the concentration of residual protein in the sample asdetermined using wet chemistry (True). The results are shown in Table 1:

TABLE 1 File Name True Prediction Difference Russia Sow Flask 8.28.84625 8.64 0.206 Russia Sow Flask 8.1 8.84625 8.798 0.0478 Russia SowFlask 8.0 8.84625 8.691 0.155 Russia Sow Flask 7.2 8.5775 8.841 −0.263Russia Sow Flask 7.1 8.5775 8.922 −0.345 Russia Sow Flask 7.0 8.57758.965 −0.387 Russia Sow Flask 6.2 10.0781 9.498 0.58 Russia Sow Flask6.1 10.0781 9.513 0.565 Russia Sow Flask 6.0 10.0781 9.522 0.556 RussiaSow Flask 5.2 8.51 8.502 0.00811 Russia Sow Flask 5.1 8.51 8.567 −0.0568Russia Sow Flask 5.0 8.51 8.646 −0.136 Russia Sow Flask 4.2 8.44 8.3140.126 Russia Sow Flask 4.1 8.44 8.303 0.137 Russia Sow Flask 4.0 8.448.446 −0.00599 Russia Sow Flask 3.2 8.495 8.738 −0.243 Russia Sow Flask3.1 8.495 8.871 −0.376

These results show the model had a high degree of predictability of theconcentration of residual protein in the samples.

While certain embodiments of the present application of the inventionhave been described, other embodiments may exist. While thespecification includes a detailed description, the invention's scope isindicated by the following claims. Furthermore, while the specificationhas been described in language specific to structural features and/ormethodological acts, the claims are not limited to the features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as illustrative aspects and embodiments of the invention.Various other aspects, embodiments, modifications, and equivalentsthereof which, after reading the description herein, may suggestthemselves to one of ordinary skill in the art without departing fromthe spirit of the present invention or the scope of the claimed subjectmatter.

What is claimed is:
 1. A method for analyzing feed for swine or poultry comprising steps of: (a) digesting in vitro a plurality of samples of animal feed each having an animal feed composition to generate a plurality of digested animal feed samples, wherein each of the plurality of digested animal feed samples comprises at least one residual component, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, gross energy, and carbohydrates, wherein the samples of animal feed consist of swine feed or poultry feed, wherein each animal feed composition comprises one or more of corn, soybean, grain(s), distiller grain(s), legumes, molasses, or a mixture of any thereof, wherein the digesting in vitro comprises at least two phases: wherein for swine feed an in vitro digestion phase is conducted for 1 to 6 hours at a pH of less than about 7 and includes use of at least one enzyme comprising pepsin, and wherein another in vitro digestion phase is conducted for 18 hours to 48 hours at neutral pH to an acidic pH of at least 6.0 and includes use of at least one enzyme comprising pancreatin, to generate the plurality of digested animal feed samples, wherein for poultry feed, an in vitro digestion phase is conducted that includes use of at least one enzyme comprising phytase, another in vitro digestion phase is conducted at a pH of less than about 7 for 30 minutes to 2 hours and includes use of at least one enzyme comprising pepsin, and an additional in vitro digestion phase is conducted at neutral pH to an acidic pH of at least 6.0 for 30 minutes to 2 hours and includes use of at least one enzyme comprising pancreatin, to generate the plurality of digested animal feed samples; (b) separating each of the plurality of digested animal feed samples obtained in step (a) into a solid component and a liquid component; (c) scanning the solid component of each of the plurality of digested animal feed samples obtained in step (b) using a near infrared (NIR) spectrophotometer to generate spectral data for each of the plurality of digested animal feed samples; (d) determining the concentration of the at least one residual component in each of the plurality of digested animal feed samples using a wet chemistry method; (e) generating a computer model comprising a computer implemented method comprising steps of: i) receiving spectral data for each of the plurality of digested animal feed samples; ii) relating the spectral data for each of the plurality of digested animal feed samples to the concentration of the at least one residual component in a corresponding sample of the plurality of digested animal feed samples; and iii) establishing a predictive relationship based on the spectral data and the concentration of the at least one residual component of the plurality of digested animal feed samples to generate the computer model; and (f) validating the computer model for analyzing feed for swine or poultry using validation samples, wherein the validation samples comprise a plurality of different samples of the animal feed that are different from the samples used in step (a), comprising comparing a concentration of at least one residual component in each of a plurality of digested samples obtained from the different samples of the animal feed that is predicted from the computer model to a corresponding measured concentration determined by wet chemistry for the at least one residual component in each of the plurality of digested samples obtained from the different samples of the animal feed.
 2. The method of claim 1, wherein the step of scanning the solid component of each of the plurality of digested feed samples further comprises the step of mathematically manipulating the spectral data of each of the plurality of digested animal feed samples.
 3. The method of claim 1, wherein the wet chemistry method comprises analyzing each sample of the digested feed for the concentration of the at least one residual component selected from the group consisting of protein, phosphorous, fat, gross energy, and carbohydrates.
 4. The method of claim 1, wherein each of the samples of animal feed is swine feed.
 5. The method of claim 4, wherein the pepsin is porcine pepsin and the pancreatin is porcine pancreatin.
 6. The method of claim 4, wherein the animal composition comprises corn.
 7. The method of claim 4, wherein the digestion phase and the other digestion phase further include use of an acetate buffer.
 8. The method of claim 1, wherein each of the samples of animal feed is poultry feed.
 9. The method of claim 8, wherein the animal composition comprises corn.
 10. The method of claim 1, wherein the at least one residual component is selected from the group consisting of protein, phosphorous, fat, and gross energy.
 11. The method of claim 1, wherein the at least one residual component is selected from the group consisting of protein, fat, and gross energy.
 12. The method of claim 1, wherein the validating in step (f) comprises: (1) digesting in vitro the plurality of different samples of the animal feed wherein digesting comprises two phases, wherein a phase is conducted at a pH of less than about 7 and includes use of at least one enzyme comprising pepsin, and wherein another phase is conducted at a neutral to acidic pH and includes at least one enzyme comprising pancreatin; (2) separating each of the plurality of digested different samples of animal feed obtained in step (1) into a solid component and a liquid component; (3) scanning the solid component obtained in step (2) of the plurality of digested different samples of animal feed using a near infrared (NIR) spectrophotometer to generate spectral data for each of the plurality of digested different samples of animal feed; (4) predicting the concentration of the at least one residual component in each of the plurality of digested different samples of animal feed using the computer model to obtain predicted concentrations thereof; (5) determining the concentration of the at least one residual component in each of the plurality of digested different samples of animal feed using a wet chemistry method to obtain measured concentrations thereof; and (6) comparing the predicted concentration of the at least one residual component in each of the plurality of digested different samples of animal feed to a corresponding measured concentration determined by the wet chemistry in making a validation determination on the computer model.
 13. The method of claim 12, wherein the separating in step (2) comprises centrifuging or filtering.
 14. The method of claim 1, further comprising: (g) determining a coefficient of determination, R² value, of the computer model based on comparing a plurality of concentrations predicted for a plurality of validation samples using the computer model with actual concentrations of the validation samples determined by wet chemistry, and (h) determining if the computer model is validated for analyzing feed for swine or poultry by determining if the R² value determined in step (g) is at least
 50. 15. The method of claim 14, wherein the determining in step (h) if the computer model is validated for analyzing feed for swine or poultry comprises determining if the R² value determined in step (g) is at least
 70. 16. The method of claim 1, wherein the separating in step (b) comprises centrifuging or filtering.
 17. The method of claim 16, further comprising freeze-drying the solid component obtained from the separating in step (b) to provide a final dry matter portion thereof prior to step (c), and wherein the solid component scanned in step (c) comprises the final dry matter portion thereof.
 18. The method of claim 16, wherein the determining using wet chemistry in step (d) is performed on the solid component of each of the plurality of digested animal feed samples. 